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SUMMARY:BSU Seminar: &quot\;Common atoms mixture models in some biostatist
 ical inference problems&quot\; - Prof Peter Mueller\, University of Texas
DTSTART:20250429T130000Z
DTEND:20250429T140000Z
UID:TALK231007@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:We consider several examples of statistical inference for two 
 or more related populations. In one example we characterize two patient po
 pulations that are relevant in the construction of a clinical study design
 \, and propose a method to adjust for detected differences. Another exampl
 e is about comparative immune profiling under two biologic conditions of i
 nterest when we identify shared versus condition-specific homogeneous cell
  subpopulations. In a third example we model spatially aligned cell subpop
 ulations for spatial transcriptomics data. \n\n\nBayesian inference in all
  three applications requires prior probability models for two or more rela
 ted distributions. We build on extensive literature on such models based o
 n Dirichlet process priors. Related models are commonly known as dependent
  Dirichlet processes (DDP)\, with many variations and extensions beyond th
 e Dirichlet process model. \n\nOne special feature in all three motivating
  applications is the focus on understanding the nature of the dependence a
 cross the related populations. In one application we aim to adjust for dif
 ferences in population heterogeneity\, in another we aim to identify and u
 nderstand homogeneous subpopulations that are characteristic for one or th
 e other condition.
LOCATION:Large Seminar Room\, East Forvie Building\, Forvie Site Robinson 
 Way Cambridge CB2 0SR.
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